566 lines
18 KiB
Python
566 lines
18 KiB
Python
from pathlib import Path
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from typing import List, Tuple
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import numpy as np
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import pandas as pd
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from quacc import plot
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from quacc.environment import env
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from quacc.utils import fmt_line_md
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def _get_metric(metric: str):
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return slice(None) if metric is None else metric
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def _get_estimators(estimators: List[str], cols: np.ndarray):
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return slice(None) if estimators is None else cols[np.in1d(cols, estimators)]
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class EvaluationReport:
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def __init__(self, name=None):
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self.data: pd.DataFrame = None
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self.fit_score = None
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self.name = name if name is not None else "default"
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def append_row(self, basep: np.ndarray | Tuple, **row):
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bp = basep[1]
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_keys, _values = zip(*row.items())
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# _keys = list(row.keys())
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# _values = list(row.values())
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if self.data is None:
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_idx = 0
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self.data = pd.DataFrame(
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{k: [v] for k, v in row.items()},
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index=pd.MultiIndex.from_tuples([(bp, _idx)]),
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columns=_keys,
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)
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return
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_idx = len(self.data.loc[(bp,), :]) if (bp,) in self.data.index else 0
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not_in_data = np.setdiff1d(list(row.keys()), self.data.columns.unique(0))
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self.data.loc[:, not_in_data] = np.nan
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self.data.loc[(bp, _idx), :] = row
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return
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@property
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def columns(self) -> np.ndarray:
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return self.data.columns.unique(0)
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@property
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def prevs(self):
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return np.sort(self.data.index.unique(0))
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class CompReport:
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def __init__(
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self,
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reports: List[EvaluationReport],
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name="default",
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train_prev=None,
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valid_prev=None,
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times=None,
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):
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self._data = (
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pd.concat(
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[er.data for er in reports],
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keys=[er.name for er in reports],
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axis=1,
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)
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.swaplevel(0, 1, axis=1)
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.sort_index(axis=1, level=0, sort_remaining=False)
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.sort_index(axis=0, level=0)
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)
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self.fit_scores = {
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er.name: er.fit_score for er in reports if er.fit_score is not None
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}
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self.train_prev = train_prev
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self.valid_prev = valid_prev
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self.times = times
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@property
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def prevs(self) -> np.ndarray:
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return np.sort(self._data.index.unique(0))
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@property
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def np_prevs(self) -> np.ndarray:
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return np.around([(1.0 - p, p) for p in self.prevs], decimals=2)
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def data(self, metric: str = None, estimators: List[str] = None) -> pd.DataFrame:
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_metric = _get_metric(metric)
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_estimators = _get_estimators(estimators, self._data.columns.unique(1))
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f_data: pd.DataFrame = self._data.copy().loc[:, (_metric, _estimators)]
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if len(f_data.columns.unique(0)) == 1:
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f_data = f_data.droplevel(level=0, axis=1)
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return f_data
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def shift_data(
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self, metric: str = None, estimators: List[str] = None
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) -> pd.DataFrame:
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shift_idx_0 = np.around(
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np.abs(
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self._data.index.get_level_values(0).to_numpy() - self.train_prev[1]
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),
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decimals=2,
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)
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shift_idx_1 = np.empty(shape=shift_idx_0.shape, dtype="<i4")
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for _id in np.unique(shift_idx_0):
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_wh = np.where(shift_idx_0 == _id)[0]
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shift_idx_1[_wh] = np.arange(_wh.shape[0], dtype="<i4")
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shift_data = self._data.copy()
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shift_data.index = pd.MultiIndex.from_arrays([shift_idx_0, shift_idx_1])
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shift_data = shift_data.sort_index(axis=0, level=0)
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_metric = _get_metric(metric)
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_estimators = _get_estimators(estimators, shift_data.columns.unique(1))
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shift_data: pd.DataFrame = shift_data.loc[:, (_metric, _estimators)]
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if len(shift_data.columns.unique(0)) == 1:
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shift_data = shift_data.droplevel(level=0, axis=1)
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return shift_data
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def avg_by_prevs(
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self, metric: str = None, estimators: List[str] = None
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) -> pd.DataFrame:
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f_dict = self.data(metric=metric, estimators=estimators)
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return f_dict.groupby(level=0).mean()
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def stdev_by_prevs(
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self, metric: str = None, estimators: List[str] = None
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) -> pd.DataFrame:
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f_dict = self.data(metric=metric, estimators=estimators)
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return f_dict.groupby(level=0).std()
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def table(self, metric: str = None, estimators: List[str] = None) -> pd.DataFrame:
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f_data = self.data(metric=metric, estimators=estimators)
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avg_p = f_data.groupby(level=0).mean()
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avg_p.loc["avg", :] = f_data.mean()
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return avg_p
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def get_plots(
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self, mode="delta", metric="acc", estimators=None, conf="default", stdev=False
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) -> List[Tuple[str, Path]]:
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if mode == "delta":
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avg_data = self.avg_by_prevs(metric=metric, estimators=estimators)
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return plot.plot_delta(
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base_prevs=self.np_prevs,
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columns=avg_data.columns.to_numpy(),
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data=avg_data.T.to_numpy(),
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metric=metric,
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name=conf,
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train_prev=self.train_prev,
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)
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elif mode == "delta_stdev":
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avg_data = self.avg_by_prevs(metric=metric, estimators=estimators)
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st_data = self.stdev_by_prevs(metric=metric, estimators=estimators)
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return plot.plot_delta(
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base_prevs=self.np_prevs,
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columns=avg_data.columns.to_numpy(),
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data=avg_data.T.to_numpy(),
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metric=metric,
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name=conf,
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train_prev=self.train_prev,
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stdevs=st_data.T.to_numpy(),
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)
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elif mode == "diagonal":
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f_data = self.data(metric=metric + "_score", estimators=estimators)
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ref: pd.Series = f_data.loc[:, "ref"]
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f_data.drop(columns=["ref"], inplace=True)
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return plot.plot_diagonal(
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reference=ref.to_numpy(),
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columns=f_data.columns.to_numpy(),
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data=f_data.T.to_numpy(),
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metric=metric,
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name=conf,
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train_prev=self.train_prev,
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)
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elif mode == "shift":
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_shift_data = self.shift_data(metric=metric, estimators=estimators)
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shift_avg = _shift_data.groupby(level=0).mean()
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shift_counts = _shift_data.groupby(level=0).count()
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shift_prevs = np.around(
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[(1.0 - p, p) for p in np.sort(shift_avg.index.unique(0))],
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decimals=2,
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)
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return plot.plot_shift(
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shift_prevs=shift_prevs,
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columns=shift_avg.columns.to_numpy(),
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data=shift_avg.T.to_numpy(),
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metric=metric,
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name=conf,
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train_prev=self.train_prev,
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counts=shift_counts.T.to_numpy(),
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)
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def to_md(self, conf="default", metric="acc", estimators=None, stdev=False) -> str:
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res = f"## {int(np.around(self.train_prev, decimals=2)[1]*100)}% positives\n"
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res += fmt_line_md(f"train: {str(self.train_prev)}")
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res += fmt_line_md(f"validation: {str(self.valid_prev)}")
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for k, v in self.times.items():
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res += fmt_line_md(f"{k}: {v:.3f}s")
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res += "\n"
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res += self.table(metric=metric, estimators=estimators).to_html() + "\n\n"
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plot_modes = np.array(["delta", "diagonal", "shift"], dtype="object")
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if stdev:
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whd = np.where(plot_modes == "delta")[0]
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if len(whd) > 0:
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plot_modes = np.insert(plot_modes, whd + 1, "delta_stdev")
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for mode in plot_modes:
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op = self.get_plots(
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mode=mode,
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metric=metric,
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estimators=estimators,
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conf=conf,
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stdev=stdev,
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)
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res += f".as_posix()})\n"
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return res
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class DatasetReport:
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def __init__(self, name, crs=None):
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self.name = name
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self.crs: List[CompReport] = [] if crs is None else crs
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def data(self, metric: str = None, estimators: str = None) -> pd.DataFrame:
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def _cr_train_prev(cr: CompReport):
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return cr.train_prev[1]
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def _cr_data(cr: CompReport):
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return cr.data(metric, estimators)
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_crs_sorted = sorted(
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[(_cr_train_prev(cr), _cr_data(cr)) for cr in self.crs],
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key=lambda cr: len(cr[1].columns),
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reverse=True,
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)
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_crs_train, _crs_data = zip(*_crs_sorted)
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_data = pd.concat(_crs_data, axis=0, keys=np.around(_crs_train, decimals=2))
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_data = _data.sort_index(axis=0, level=0)
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return _data
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def shift_data(self, metric: str = None, estimators: str = None) -> pd.DataFrame:
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_shift_data: pd.DataFrame = pd.concat(
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sorted(
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[cr.shift_data(metric, estimators) for cr in self.crs],
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key=lambda d: len(d.columns),
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reverse=True,
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),
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axis=0,
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)
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shift_idx_0 = _shift_data.index.get_level_values(0)
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shift_idx_1 = np.empty(shape=shift_idx_0.shape, dtype="<i4")
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for _id in np.unique(shift_idx_0):
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_wh = np.where(shift_idx_0 == _id)[0]
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shift_idx_1[_wh] = np.arange(_wh.shape[0])
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_shift_data.index = pd.MultiIndex.from_arrays([shift_idx_0, shift_idx_1])
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_shift_data = _shift_data.sort_index(axis=0, level=0)
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return _shift_data
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def add(self, cr: CompReport):
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if cr is None:
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return
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self.crs.append(cr)
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def __add__(self, cr: CompReport):
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if cr is None:
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return
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return DatasetReport(self.name, crs=self.crs + [cr])
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def __iadd__(self, cr: CompReport):
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self.add(cr)
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return self
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def to_md(self, conf="default", metric="acc", estimators=[], stdev=False):
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res = f"# {self.name}\n\n"
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for cr in self.crs:
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res += f"{cr.to_md(conf, metric=metric, estimators=estimators, stdev=stdev)}\n\n"
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_data = self.data(metric=metric, estimators=estimators)
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_shift_data = self.shift_data(metric=metric, estimators=estimators)
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res += "## avg\n"
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######################## avg on train ########################
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res += "### avg on train\n"
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avg_on_train = _data.groupby(level=1).mean()
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prevs_on_train = np.sort(avg_on_train.index.unique(0))
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stdev_on_train = _data.groupby(level=1).std() if stdev else None
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avg_on_train_tbl = _data.groupby(level=1).mean()
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avg_on_train_tbl.loc["avg", :] = _data.mean()
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res += avg_on_train_tbl.to_html() + "\n\n"
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delta_op = plot.plot_delta(
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base_prevs=np.around([(1.0 - p, p) for p in prevs_on_train], decimals=2),
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columns=avg_on_train.columns.to_numpy(),
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data=avg_on_train.T.to_numpy(),
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metric=metric,
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name=conf,
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train_prev=None,
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avg="train",
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)
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res += f".as_posix()})\n"
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if stdev:
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delta_stdev_op = plot.plot_delta(
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base_prevs=np.around(
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[(1.0 - p, p) for p in prevs_on_train], decimals=2
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),
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columns=avg_on_train.columns.to_numpy(),
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data=avg_on_train.T.to_numpy(),
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metric=metric,
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name=conf,
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train_prev=None,
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stdevs=stdev_on_train.T.to_numpy(),
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avg="train",
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)
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res += f".as_posix()})\n"
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######################## avg on test ########################
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res += "### avg on test\n"
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avg_on_test = _data.groupby(level=0).mean()
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prevs_on_test = np.sort(avg_on_test.index.unique(0))
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stdev_on_test = _data.groupby(level=0).std() if stdev else None
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avg_on_test_tbl = _data.groupby(level=0).mean()
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avg_on_test_tbl.loc["avg", :] = _data.mean()
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res += avg_on_test_tbl.to_html() + "\n\n"
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delta_op = plot.plot_delta(
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base_prevs=np.around([(1.0 - p, p) for p in prevs_on_test], decimals=2),
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columns=avg_on_test.columns.to_numpy(),
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data=avg_on_test.T.to_numpy(),
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metric=metric,
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name=conf,
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train_prev=None,
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avg="test",
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)
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res += f".as_posix()})\n"
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if stdev:
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delta_stdev_op = plot.plot_delta(
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base_prevs=np.around([(1.0 - p, p) for p in prevs_on_test], decimals=2),
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columns=avg_on_test.columns.to_numpy(),
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data=avg_on_test.T.to_numpy(),
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metric=metric,
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name=conf,
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train_prev=None,
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stdevs=stdev_on_test.T.to_numpy(),
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avg="test",
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)
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res += f".as_posix()})\n"
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######################## avg shift ########################
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res += "### avg dataset shift\n"
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avg_shift = _shift_data.groupby(level=0).mean()
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count_shift = _shift_data.groupby(level=0).count()
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prevs_shift = np.sort(avg_shift.index.unique(0))
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shift_op = plot.plot_shift(
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shift_prevs=np.around([(1.0 - p, p) for p in prevs_shift], decimals=2),
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columns=avg_shift.columns.to_numpy(),
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data=avg_shift.T.to_numpy(),
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metric=metric,
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name=conf,
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train_prev=None,
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counts=count_shift.T.to_numpy(),
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)
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res += f".as_posix()})\n"
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return res
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def __iter__(self):
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return (cr for cr in self.crs)
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def __test():
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df = None
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print(f"{df is None = }")
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if df is None:
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bp = 0.75
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idx = 0
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d = {"a": 0.0, "b": 0.1}
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df = pd.DataFrame(
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d,
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index=pd.MultiIndex.from_tuples([(bp, idx)]),
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columns=d.keys(),
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)
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print(df)
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print("-" * 100)
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bp = 0.75
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idx = len(df.loc[bp, :])
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df.loc[(bp, idx), :] = {"a": 0.2, "b": 0.3}
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print(df)
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print("-" * 100)
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bp = 0.90
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idx = len(df.loc[bp, :]) if bp in df.index else 0
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df.loc[(bp, idx), :] = {"a": 0.2, "b": 0.3}
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print(df)
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print("-" * 100)
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bp = 0.90
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idx = len(df.loc[bp, :]) if bp in df.index else 0
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d = {"a": 0.2, "v": 0.3, "e": 0.4}
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notin = np.setdiff1d(list(d.keys()), df.columns)
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df.loc[:, notin] = np.nan
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df.loc[(bp, idx), :] = d
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print(df)
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print("-" * 100)
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bp = 0.90
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idx = len(df.loc[bp, :]) if bp in df.index else 0
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d = {"a": 0.3, "v": 0.4, "e": 0.5}
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notin = np.setdiff1d(list(d.keys()), df.columns)
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print(f"{notin = }")
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df.loc[:, notin] = np.nan
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df.loc[(bp, idx), :] = d
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print(df)
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print("-" * 100)
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print(f"{np.sort(np.unique(df.index.get_level_values(0))) = }")
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print("-" * 100)
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print(f"{df.loc[(0.75, ),:] = }\n")
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print(f"{df.loc[(slice(None), 1),:] = }")
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print("-" * 100)
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print(f"{(0.75, ) in df.index = }")
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print(f"{(0.7, ) in df.index = }")
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print("-" * 100)
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df1 = pd.DataFrame(
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{
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"a": np.linspace(0.0, 1.0, 6),
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"b": np.linspace(1.0, 2.0, 6),
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"e": np.linspace(2.0, 3.0, 6),
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"v": np.linspace(0.0, 1.0, 6),
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},
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index=pd.MultiIndex.from_product([[0.75, 0.9], [0, 1, 2]]),
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columns=["a", "b", "e", "v"],
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)
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df2 = (
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pd.concat([df, df1], keys=["a", "b"], axis=1)
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.swaplevel(0, 1, axis=1)
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.sort_index(axis=1, level=0)
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)
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df3 = pd.concat([df1, df], keys=["b", "a"], axis=1)
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print(df)
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print(df1)
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print(df2)
|
|
print(df3)
|
|
df = df3
|
|
print("-" * 100)
|
|
|
|
print(df.loc[:, ("b", ["e", "v"])])
|
|
print(df.loc[:, (slice(None), ["e", "v"])])
|
|
print(df.loc[:, ("b", slice(None))])
|
|
print(df.loc[:, ("b", slice(None))].droplevel(level=0, axis=1))
|
|
print(df.loc[:, (slice(None), ["e", "v"])].droplevel(level=0, axis=1))
|
|
print(len(df.loc[:, ("b", slice(None))].columns.unique(0)))
|
|
print("-" * 100)
|
|
|
|
idx_0 = np.around(np.abs(df.index.get_level_values(0).to_numpy() - 0.8), decimals=2)
|
|
midx = pd.MultiIndex.from_arrays([idx_0, df.index.get_level_values(1)])
|
|
print(midx)
|
|
dfs = df.copy()
|
|
dfs.index = midx
|
|
print(df)
|
|
print(dfs)
|
|
print("-" * 100)
|
|
|
|
df.loc[(0.85, 0), :] = np.linspace(0, 1, 8)
|
|
df.loc[(0.85, 1), :] = np.linspace(0, 1, 8)
|
|
df.loc[(0.85, 2), :] = np.linspace(0, 1, 8)
|
|
idx_0 = np.around(np.abs(df.index.get_level_values(0).to_numpy() - 0.8), decimals=2)
|
|
print(np.where(idx_0 == 0.05))
|
|
idx_1 = np.empty(shape=idx_0.shape, dtype="<i4")
|
|
print(idx_1)
|
|
for _id in np.unique(idx_0):
|
|
wh = np.where(idx_0 == _id)[0]
|
|
idx_1[wh] = np.arange(wh.shape[0])
|
|
midx = pd.MultiIndex.from_arrays([idx_0, idx_1])
|
|
dfs = df.copy()
|
|
dfs.index = midx
|
|
dfs.sort_index(level=0, axis=0, inplace=True)
|
|
print(df)
|
|
print(dfs)
|
|
print("-" * 100)
|
|
|
|
print(np.sort(dfs.index.unique(0)))
|
|
print("-" * 100)
|
|
|
|
print(df.groupby(level=0).mean())
|
|
print(dfs.groupby(level=0).mean())
|
|
print("-" * 100)
|
|
|
|
s = df.mean(axis=0)
|
|
dfa = df.groupby(level=0).mean()
|
|
dfa.loc["avg", :] = s
|
|
print(dfa)
|
|
print("-" * 100)
|
|
|
|
print(df)
|
|
dfn = df.loc[:, (slice(None), slice(None))]
|
|
print(dfn)
|
|
print(f"{df is dfn = }")
|
|
print("-" * 100)
|
|
|
|
a = np.array(["abc", "bcd", "cde", "bcd"], dtype="object")
|
|
print(a)
|
|
whb = np.where(a == "bcd")[0]
|
|
if len(whb) > 0:
|
|
a = np.insert(a, whb + 1, "pippo")
|
|
print(a)
|
|
print("-" * 100)
|
|
|
|
dff: pd.DataFrame = df.loc[:, ("a",)]
|
|
print(dff.to_dict(orient="list"))
|
|
dff = dff.drop(columns=["v"])
|
|
print(dff)
|
|
s: pd.Series = dff.loc[:, "e"]
|
|
print(s)
|
|
print(s.to_numpy())
|
|
print(type(s.to_numpy()))
|
|
print("-" * 100)
|
|
|
|
df3 = pd.concat([df, df], axis=0, keys=[0.5, 0.3]).sort_index(axis=0, level=0)
|
|
print(df3)
|
|
df3n = pd.concat([df, df], axis=0).sort_index(axis=0, level=0)
|
|
print(df3n)
|
|
df = df3
|
|
print("-" * 100)
|
|
|
|
print(df.groupby(level=1).mean(), df.groupby(level=1).count())
|
|
print("-" * 100)
|
|
|
|
print(df)
|
|
for ls in df.T.to_numpy():
|
|
print(ls)
|
|
print("-" * 100)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
__test()
|